Türkiye’de Evlenme ve Ekonomik Büyüme Arasındaki İlişki: Nedensellik Analizi
Bibliographic record
Abstract
Man is a social being who, by nature, cannot live alone and needs other people. When evaluated in the context of male-female relations, one of the ways to establish a togetherness between couples is the marriage of the parties. Healthy marriages can lead to emergence strong families and strong societies in every field. Marriage allows the family, which is the smallest building block that constitutes a society, to be officially established and to have children in the following years of marriage, thus increasing the population of the country and thus the production and employment levels. Ensuring the continuity of the family necessitates the joint taking of basic economic decisions such as consumption and investment. Since these decisions also affect growth, the relationship between marriage and economic growth needs to be examined. From this point of view, the aim of the study is to reveal the causal relationship between marriage and economic growth. In the study, where marriage is represented by the number of marriages and economic growth is also represented by Gross Domestic Product, a Granger causality test based on the VAR system was conducted by using the quarterly data for the 2001-2021 period in Turkey as a percentage change compared to same quarter in previous year. According to the empirical results obtained; A bidirectional causality relationship was found between marriage and growth at the 5% significance level. That is, while marriage is the Granger cause of economic growth, economic growth is also the Granger cause of marriage. This result reveals that union of marriage can have positive effects on the Turkish economy and that economic growth can enable individuals to get married.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".